A new classification method based on the negation of a basic probability assignment in the evidence theory

Wu, Dongrui; Liu, Zijing; Tang, Yongchuan

doi:10.1016/j.engappai.2020.103985

Cited by 43 publications

(23 citation statements)

References 73 publications

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“…The similar feature-extraction procedure can be implemented and used in same way. In addition, the entropy-based method is typical for uncertain information measuring, thus further researches can be carried out in the following areas: (i) some current tasks about entropy-based classification [31]- [33] can be expanded to solve potential conflict of uncertain information in fault diagnosis; (ii) the D-S theory can be well-used for modeling and processing of uncertain information in fault classification, for example the reliability of data source in D-S theory framework [34], and the ambiguity measure or belief entropy [35]; (iii) the fusion on conflict information is important for the future research in fault diagnosis, about which some current works [36], [37] should be paid attention to and extensively studied.…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Jiao

Jiang

et al. 2021

IEEE Access

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Section: Discussionmentioning

confidence: 99%

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Jiao

Jiang

et al. 2021

IEEE Access

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“…is paper adopts the classical fusion rule, but when there is a large amount of data, there may be conflicts between evidence, so this rule will no longer be applicable. erefore, a new basic probability assignment method proposed by Jing et al can be considered to adopt [33] and a new classification method based on the negation of a basic probability assignment in the evidence theory proposed by Wu et al [34] to resolve possible conflict information fusion in the method.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution

Shi

et al. 2021

Discrete Dynamics in Nature and Society

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In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the conjugate Dirichlet distribution of multinomial distribution is selected as a prior distribution, and Dempster–Shafer evidence theory (D-S theory) is introduced to fuse multisource previous information. Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. The study result shows that the Bayesian inference method based on multinomial distribution is highly maneuverable and can be used to predict ammunition demand of different damage grades under the condition of small samples.

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“…Iris data set is a widely used classification data set at present. [37][38][39] In the iris data set, there are three species of iris flowers S e , V c , and V i , which constitute the FOD S V V Θ = { , , } e c i in the classification problem. Irises in each category contain four attributes, sepal length (SL), sepal width (SW), petal length (PL), and petal width (PW).…”

Section: Iris Classification Problemmentioning

confidence: 99%

Measuring total uncertainty in evidence theory

Deng

Wang

2021

Int J Intell Syst

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Dempster-Shafer (DS) evidence theory is the most significant and effective method for uncertainty modeling and reasoning. How to measure the uncertainty in DS evidence theory precisely remains an outstanding problem. Various types of uncertainty measures for evidence have been presented. However, they all suffer some limitations. To address this issue, we propose a novel total uncertainty measure for the DS evidence theory framework that can quantify the uncertainty in the evidence. The new total uncertainty measure uses the Hellinger distance between the belief interval of every singleton and the most uncertain interval. Compared with the existing uncertainty measurement methods, the proposed approach is more sensitive to changes in evidence. The effectiveness and rationality of the brand new total uncertainty measure are illustrated by numerical examples and practical applications.

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A new classification method based on the negation of a basic probability assignment in the evidence theory

Cited by 43 publications

References 73 publications

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution

Measuring total uncertainty in evidence theory

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